FIRST-ORDER ASYMPTOTIC THEORY FOR PARAMETRIC MISSPECIFICATION TESTS OF GARCH MODELS
GARCH 模型参数设定误检验的一阶渐近理论
This paper develops a framework for the construction and analysis of parametric misspecification tests for generalized autoregressive conditional heteroskedastic (GARCH) models, based on first-order asymptotic theory. The principal finding is that estimation effects from the correct specification of the conditional mean (regression) function can be asymptotically nonnegligible. This implies that certain procedures, such as the asymmetry tests of Engle and Ng (1993, Journal…